In [1]:
import pandas as pd
import seaborn as sns
import plotly.express as px

import matplotlib.pyplot as plt
In [2]:
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib¶

For this excercise, we have written the following code to load the stock dataset built into plotly express.

In [3]:
stocks = px.data.stocks()
stocks.head()
Out[3]:
date GOOG AAPL AMZN FB NFLX MSFT
0 2018-01-01 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 2018-01-08 1.018172 1.011943 1.061881 0.959968 1.053526 1.015988
2 2018-01-15 1.032008 1.019771 1.053240 0.970243 1.049860 1.020524
3 2018-01-22 1.066783 0.980057 1.140676 1.016858 1.307681 1.066561
4 2018-01-29 1.008773 0.917143 1.163374 1.018357 1.273537 1.040708

Question 1:¶

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

In [4]:
fig, goog = plt.subplots(figsize=(12,10))

goog.plot(stocks.date, stocks.GOOG, label = "GOOG")

goog.set_title('Stock price of GOOG')
goog.set_xlabel('Date [yyyy/mm/dd]')
goog.set_ylabel('Stocks value [€]')
goog.set_xticks([0,14,29,44,59,74,89,104])

goog.legend()

plt.show()

Question 2:¶

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

In [5]:
fig, goog = plt.subplots(figsize=(12,10))

goog.plot(stocks.date, stocks.GOOG, label = "GOOG")
goog.plot(stocks.date, stocks.AAPL, label = "AAPL")
goog.plot(stocks.date, stocks.AMZN, label = "AMZN")
goog.plot(stocks.date, stocks.FB, label = "FB")
goog.plot(stocks.date, stocks.NFLX, label = "NFLX")
goog.plot(stocks.date, stocks.MSFT, label = "MSFT", linestyle='dashdot', marker='o')

goog.set_title('Stock prices')
goog.set_xlabel('Date [yyyy/mm/dd]')
goog.set_ylabel('Stocks value [€]')
goog.set_xticks([0,14,29,44,59,74,89,104])

goog.legend()

plt.show()

Seaborn¶

First, load the tips dataset

In [6]:
tips = sns.load_dataset('tips')
tips.head()
Out[6]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4

Question 3:¶

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

  • Are there differences between male and female when it comes to giving tips?
  • What attribute correlate the most with tip?
In [7]:
print('What is the difference in tipping between male and female?')

sns.jointplot(x='sex', y='tip', data=tips)

plt.show()
What is the difference in tipping between male and female?

Plotly Express¶

Question 4:¶

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset¶

Hints:

  • Turn stocks dataframe into a structure that can be picked up easily with plotly express
In [8]:
fig = px.line(stocks, 
              x='date', 
              y=['GOOG','AAPL','AMZN','FB','NFLX','MSFT'],
              title='Stocks values'
             )
fig.show()

The tips dataset¶

In [9]:
fig = px.histogram(tips,
                   x='total_bill',
                   y='tip',
                   color='sex', 
                   hover_data=tips.columns
                  )
fig.show()

Question 5:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [10]:
#load data 
df = px.data.gapminder()
df.head()
Out[10]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4
In [11]:
df_2007 = df.query('year==2007')

df_2007_new = df_2007.groupby('continent').sum()

fig = px.bar(df_2007_new, 
            x="pop", 
            y=df_2007_new.index, 
            orientation='h',
            color=df_2007_new.index)

fig.update_yaxes(categoryorder='total descending')

fig.show()
In [ ]: